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Quick fix for some easy issues before release #734

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merged 1 commit into from
Sep 6, 2024
Merged

Quick fix for some easy issues before release #734

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2 changes: 2 additions & 0 deletions python/src/spark_rapids_ml/classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -831,6 +831,8 @@ class LogisticRegression(
And it will automatically map pyspark parameters
to cuML parameters.

In the case of applying LogisticRegression on sparse vectors, Spark 3.4 or above is required.

Parameters
----------
featuresCol: str or List[str]
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12 changes: 8 additions & 4 deletions python/src/spark_rapids_ml/knn.py
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Expand Up @@ -851,7 +851,11 @@ def setAlgorithm(self: P, value: str) -> P:
"""
Sets the value of `algorithm`.
"""
assert value == "ivfflat", "Only IVFFLAT algorithm is currently supported"
assert value in {
"ivfflat",
"ivfpq",
"cagra",
}, "Only ivfflat, ivfpq, and cagra are currently supported"
self._set_params(algorithm=value)
return self

Expand Down Expand Up @@ -919,7 +923,7 @@ class ApproximateNearestNeighbors(
the default number of approximate nearest neighbors to retrieve for each query.

algorithm: str (default = 'ivfflat')
the algorithm parameter to be passed into cuML. It currently must be 'ivfflat' or 'ivfpq'. Other algorithms are expected to be supported later.
the algorithm parameter to be passed into cuML. It currently must be 'ivfflat', 'ivfpq' or 'cagra'. Other algorithms are expected to be supported later.

algoParams: Optional[Dict[str, Any]] (default = None)
if set, algoParam is used to configure the algorithm, on each data partition (or maxRecordsPerBatch if Arrow is enabled) of the item_df.
Expand Down Expand Up @@ -1455,7 +1459,7 @@ def _transform_internal(

start_time = time.time()

if nn_object is not "cagra":
if nn_object != "cagra":
nn_object.fit(item)
else:
from cuvs.neighbors import cagra
Expand All @@ -1473,7 +1477,7 @@ def _transform_internal(

start_time = time.time()

if nn_object is not "cagra":
if nn_object != "cagra":
distances, indices = nn_object.kneighbors(bcast_qfeatures.value)
else:
gpu_qfeatures = cp.array(
Expand Down